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Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

  • Define a Function that Makes a Graph
  • Question 1: Use yfinance to Extract Stock Data
  • Question 2: Use Webscraping to Extract Tesla Revenue Data
  • Question 3: Use yfinance to Extract Stock Data
  • Question 4: Use Webscraping to Extract GME Revenue Data
  • Question 5: Plot Tesla Stock Graph
  • Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min

In [ ]:
!pip install yfinance==0.1.67
!mamba install bs4==4.10.0 -y
!pip install nbformat==4.2.0

Collecting yfinance==0.1.67
  Downloading yfinance-0.1.67-py2.py3-none-any.whl (25 kB)
Requirement already satisfied: pandas>=0.24 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (1.3.5)
Requirement already satisfied: numpy>=1.15 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (1.21.6)
Requirement already satisfied: requests>=2.20 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (2.29.0)
Collecting multitasking>=0.0.7 (from yfinance==0.1.67)
  Downloading multitasking-0.0.11-py3-none-any.whl (8.5 kB)
Requirement already satisfied: lxml>=4.5.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (4.9.2)
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Installing collected packages: multitasking, yfinance
Successfully installed multitasking-0.0.11 yfinance-0.1.67

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In [ ]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

Define Graphing Function¶

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

In [ ]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
In [12]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [ ]:

Question 1: Use yfinance to Extract Stock Data¶

In [ ]:

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

In [15]:
tesla = yf.Ticker("TSLA")

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to max so we get information for the maximum amount of time.

In [16]:
tesla_data = tesla.history(period="max")

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

In [17]:
tesla_data.reset_index(inplace=True)
tesla_data.head()
Out[17]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 1.266667 1.666667 1.169333 1.592667 281494500 0 0.0
1 2010-06-30 1.719333 2.028000 1.553333 1.588667 257806500 0 0.0
2 2010-07-01 1.666667 1.728000 1.351333 1.464000 123282000 0 0.0
3 2010-07-02 1.533333 1.540000 1.247333 1.280000 77097000 0 0.0
4 2010-07-06 1.333333 1.333333 1.055333 1.074000 103003500 0 0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.

In [19]:
url = "https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue"
html_data = requests.get(url).text

Parse the html data using beautiful_soup.

In [20]:
soup = BeautifulSoup(html_data, "html.parser")
soup.find_all('title')
Out[20]:
[<title>Tesla Revenue 2010-2023 | TSLA | MacroTrends</title>]

Using BeautifulSoup or the read_html function extract the table with Tesla Quarterly Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.

Click here if you need help locating the table 

Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab

soup.find_all("tbody")[1]

If you want to use the read_html function the table is located at index 1
In [21]:
tesla_revenue = pd.DataFrame(columns = ['Date', 'Revenue'])

for row in soup.find_all("tbody")[1].find_all("tr"):
    col = row.find_all("td")
    date = col[0].text
    revenue = col[1].text.replace("$", "").replace(",", "")
    
    tesla_revenue = tesla_revenue.append({"Date": date, "Revenue": revenue}, ignore_index = True)

Execute the following line to remove the comma and dollar sign from the Revenue column.

In [ ]:
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")

Execute the following lines to remove an null or empty strings in the Revenue column.

In [ ]:
tesla_revenue.dropna(inplace=True)

tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

In [22]:
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
In [23]:
tesla_revenue.tail()
Out[23]:
Date Revenue
50 2010-09-30 31
51 2010-06-30 28
52 2010-03-31 21
54 2009-09-30 46
55 2009-06-30 27

Question 3: Use yfinance to Extract Stock Data¶

In [24]:
GameStop = yf.Ticker("GME")
In [25]:
gme_data = GameStop.history(period = 'max')

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

In [26]:
gme_data.reset_index(inplace = True)
gme_data.head()
Out[26]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 1.620128 1.693350 1.603296 1.691666 76216000 0.0 0.0
1 2002-02-14 1.712707 1.716074 1.670626 1.683250 11021600 0.0 0.0
2 2002-02-15 1.683250 1.687458 1.658001 1.674834 8389600 0.0 0.0
3 2002-02-19 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to max so we get information for the maximum amount of time.

In [ ]:
url = "https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue"
html_data = requests.get(url).text

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

In [ ]:

Question 4: Use Webscraping to Extract GME Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data.

In [ ]:

Parse the html data using beautiful_soup.

In [27]:
soup = BeautifulSoup(html_data, "html.parser")
soup.find_all('title')
Out[27]:
[<title>Tesla Revenue 2010-2023 | TSLA | MacroTrends</title>]

Using BeautifulSoup or the read_html function extract the table with GameStop Quarterly Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column using a method similar to what you did in Question 2.

Click here if you need help locating the table 

Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab

soup.find_all("tbody")[1]

If you want to use the read_html function the table is located at index 1
In [28]:
gme_revenue = pd.DataFrame(columns = ['Date', 'Revenue'])

for row in soup.find_all("tbody")[1].find_all("tr"):
    col = row.find_all("td")
    date = col[0].text
    revenue = col[1].text.replace("$", "").replace(",", "")
    
    gme_revenue = gme_revenue.append({"Date": date, "Revenue": revenue}, ignore_index = True)

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

In [29]:
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
gme_revenue.tail()
Out[29]:
Date Revenue
51 2010-06-30 28
52 2010-03-31 21
53 2009-12-31
54 2009-09-30 46
55 2009-06-30 27

Question 5: Plot Tesla Stock Graph¶

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(tesla_data, tesla_revenue, 'Tesla'). Note the graph will only show data upto June 2021.

In [30]:
make_graph(tesla_data, tesla_revenue, 'Tesla')

Question 6: Plot GameStop Stock Graph¶

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.

In [35]:
make_graph(gme_data, gme_revenue, 'GameStop')

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_67/1051334688.py in <module>
----> 1 make_graph(gme_data, gme_revenue, 'GameStop')

/tmp/ipykernel_67/2068038883.py in make_graph(stock_data, revenue_data, stock)
      4     revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
      5     fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
----> 6     fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
      7     fig.update_xaxes(title_text="Date", row=1, col=1)
      8     fig.update_xaxes(title_text="Date", row=2, col=1)

~/conda/envs/python/lib/python3.7/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors)
   5813         else:
   5814             # else, only a single dtype is given
-> 5815             new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
   5816             return self._constructor(new_data).__finalize__(self, method="astype")
   5817 

~/conda/envs/python/lib/python3.7/site-packages/pandas/core/internals/managers.py in astype(self, dtype, copy, errors)
    416 
    417     def astype(self: T, dtype, copy: bool = False, errors: str = "raise") -> T:
--> 418         return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
    419 
    420     def convert(

~/conda/envs/python/lib/python3.7/site-packages/pandas/core/internals/managers.py in apply(self, f, align_keys, ignore_failures, **kwargs)
    325                     applied = b.apply(f, **kwargs)
    326                 else:
--> 327                     applied = getattr(b, f)(**kwargs)
    328             except (TypeError, NotImplementedError):
    329                 if not ignore_failures:

~/conda/envs/python/lib/python3.7/site-packages/pandas/core/internals/blocks.py in astype(self, dtype, copy, errors)
    589         values = self.values
    590 
--> 591         new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
    592 
    593         new_values = maybe_coerce_values(new_values)

~/conda/envs/python/lib/python3.7/site-packages/pandas/core/dtypes/cast.py in astype_array_safe(values, dtype, copy, errors)
   1307 
   1308     try:
-> 1309         new_values = astype_array(values, dtype, copy=copy)
   1310     except (ValueError, TypeError):
   1311         # e.g. astype_nansafe can fail on object-dtype of strings

~/conda/envs/python/lib/python3.7/site-packages/pandas/core/dtypes/cast.py in astype_array(values, dtype, copy)
   1255 
   1256     else:
-> 1257         values = astype_nansafe(values, dtype, copy=copy)
   1258 
   1259     # in pandas we don't store numpy str dtypes, so convert to object

~/conda/envs/python/lib/python3.7/site-packages/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy, skipna)
   1199     if copy or is_object_dtype(arr.dtype) or is_object_dtype(dtype):
   1200         # Explicit copy, or required since NumPy can't view from / to object.
-> 1201         return arr.astype(dtype, copy=True)
   1202 
   1203     return arr.astype(dtype, copy=copy)

ValueError: could not convert string to float:

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Azim Hirjani

Change Log¶

Date (YYYY-MM-DD) Version Changed By Change Description
2022-02-28 1.2 Lakshmi Holla Changed the URL of GameStop
2020-11-10 1.1 Malika Singla Deleted the Optional part
2020-08-27 1.0 Malika Singla Added lab to GitLab

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